The autopsy is the ultimate and potentially most complete form of follow-up in medicine. Effective use of large amounts of clinical and pathologic data from series of autopsied patients requires computer processing. Many clinicopathologic sutides interpret patient information as cause and effect relationships. This study will enlarge and computerize an existing index of autopsied patients, then subject it to cause effect analysis using the formal, mathematical language of symbolic logic. A standardized questionnaire will be filled out prospectively for all new autopsies and retrospectively for selected autopsies. This questionnaire will include diagnostic impressions (qualitative data), clinical, gross pathologic, and microscopic grading (semiquantitative data), and laboratory and morphometric observations (quantitative data). Specific cause and effect problems commonly observed at autopsy will be studied. First, what are the probable causes of evident effects (death itself; the pattern of cancer metastases)? Second, what are the probable effects of evident causes (cancer chemotherapy; surgical procedures)? Finally, what is the direction of cause and effect for associated findings (coronary thrombosis and myocardial infarction; diabetes mellitus and hypertension)? The "certainty levels calculus" is an extended form of symbolic logic where statements may have a graded scale of certainty. Statements whose true-false status is unobtainable or only indirectly known are easily handled. In the proposed study, we shall translate cause and effect hypotheses into certainty levels language, then test them against observations in the patient data index. This study will: (1) determine what observations must be collected and indexed to support cause and effect interpretations of autopsy data; (2) develop computer programs, compatible with an existing statistical package, which perform a formal cause and effect analysis; and (3) extend ordinary symbolic logic for specialized applications in medicine. This study will augment the interpretive powers of clinicopathologic correlation by combining the rigor of a formal language with the data management capabilities of a computer.